Decentralized sliding mode adaptive controller design based on fuzzy neural networks for interconnected uncertain nonlinear systems

2000 ◽  
Vol 11 (6) ◽  
pp. 1471-1480 ◽  
Author(s):  
Feipeng Da
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Controller Design ◽  
Sliding Mode ◽  
Adaptive Controller ◽  
Fuzzy Neural Networks ◽  
Uncertain Nonlinear Systems ◽  
Fuzzy Neural

Hybrid fuzzy direct/indirect adaptive controller for uncertain nonlinear systems

2020 ◽  
Vol 42 (15) ◽  
pp. 3012-3023
Author(s):  
Youssouf Bibi ◽  
Omar Bouhali ◽  
Tarek Bouktir
Keyword(s):  
Fuzzy Logic ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Fuzzy Logic Controller ◽  
Adaptive Controller ◽  
Fuzzy Neural Networks ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Controllers ◽  
Direct Adaptive Control ◽  
Fuzzy Neural

This paper describes a new approach to adaptive control of uncertain nonlinear systems. A fuzzy logic controller is used to combine both direct and indirect methods. Based on the fuzzy neural networks, the plant unknown nonlinear functions are estimated, and then combined to form the indirect control law. In parallel, another fuzzy neural network approximates the direct adaptive control. According to the modelling error and its derivatives, the fuzzy logic controller modulates between direct and indirect adaptive controllers. The global stability of the overall system is shown by constructing a Lyapunov function. The simulation results show that within this scheme, the control objectives can be achieved with a fast convergence and optimal control for different dynamic regimes.

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

scholarly journals Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tat-Bao-Thien Nguyen ◽  
Teh-Lu Liao ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Adaptive Sliding Mode Control ◽  
Input Nonlinearity ◽  
Mode Control ◽  
Model Dynamics ◽  
Fuzzy Neural

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.

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